This invention relates generally to translation systems and more particularly real-time cross-lingual communication methods and systems.
Language differences constitute barriers that block communication everywhere. In enterprises, language is a barrier to contacts with foreign-speaking customers, partners, distributors, sales reps, employees, colleagues, research collaborators, foreign government counterparts, etc. In hospitals, it is a barrier between foreign-speaking patients and doctors. In the military, the language barrier impedes sharing updated information moment-to-moment among members of a multinational force. In the personal world, it is a barrier to communicating with overseas relatives, service providers, etc.
Human interpreters provide way of addressing language differences in live meetings and phone calls. However, such specialists are difficult to schedule and prohibitively expensive for spontaneous and/or informal communications. Similarly, human translators for written communications are very expensive, and may introduce unacceptably long delays. Currently available machine translation tools are unreliable, and the benefits of real-time communication and direct human contact are often lost. Furthermore, when currently available, relatively fragile translation tools are combined with similarly imperfect automatic speech recognition systems, errors are compounded in each step of processing, to the point where the result is often unintelligible. Such speech-to-speech machine translation systems permit little or no user intervention in the speech recognition or translation processes, beyond rewriting the input sentence. Thus the user has no control over how the system recognizes or translates a given spoken sentence, and the errors that result undermine the user's confidence in computer-mediated communication.
One particular obstacle for machine translation quality is the problem of word-sense disambiguation, i.e., of correctly identifying the sense in which a given word was intended in the input sentence. Existing automatic techniques have proven insufficient for disambiguation because 1) many sentences in common usage are elliptical or ambiguous and 2) semantically rich dictionary information that is necessary for accurate translation is available only in limited quantities, from diverse sources, and in diverse implementations.
Further exacerbating the problem is the fact that machine translation systems for different languages are developed by different providers, with diverse and often incompatible interfaces and implementations, making installation, maintenance, and use infeasible. The upshot is that no convenient, cost-effective solution currently exists for bridging the language gap, especially for impromptu or informal communication situations.
As a result, there is a need to solve the problems of the prior art to providing accurate and real-time machine translation.